Proposed by [1]}, the Task-aligned One-stage Object Detection (TOOD) has two important contributions over the existing one-stage object detector models. First, instead of using parallel heads for classification and localization, TOOD utilizes a single, so-called task-aligned head in order to reduce any spatial misalignment. Second, TOOD uses a novel learning scheme, namely task alignment learning, which yields optimal anchor estimates for both classification and localization tasks. Although it is not one of the backbones tested for TOOD in the original paper, we again utilized ResNet-50 on this architecture, for a fair comparison with the VfNet.
